A cross-region prediction model named SeisEML (an acronym for Seismological Ensemble
Machine Learning) has been developed in this paper to predict the peak ground …

This article provides an overview of current applications of machine learning (ML) in
seismology. ML techniques are becoming increasingly widespread in seismology, with …

Over the past decades, researchers have given increasing attention to the modelling of the
spatial correlation of earthquake ground motion intensity measures (IMs), particularly when …

Subduction zone earthquakes result in some of the most devastating natural hazards on
Earth. Knowledge of where great (moment magnitude M≥ 8) subduction zone earthquakes …

To complement the new European Strong-Motion dataset and the ongoing efforts to update
the seismic hazard and risk assessment of Europe and Mediterranean regions, we propose …

The fundamental objective of earthquake engineering is to protect lives and livelihoods
through the reduction of seismic risk. Directly or indirectly, this generally requires …

The use of strongly biased data generally leads to large distortions in a trained machine
learning model. We face this problem when constructing a predictor for earthquake …

Theoretical and observational studies suggest that between‐event variability in the median
ground motions of larger (M≥ 5) earthquakes is controlled primarily by the dynamic …

Abstract Distributed Acoustic Sensing (DAS) is a promising technique to improve the rapid
detection and characterization of earthquakes. Previous DAS studies mainly focus on the …

Residuals between ground‐motion data and ground‐motion prediction equations (GMPEs)
can be decomposed into terms representing earthquake source, path, and site effects …